高光谱土壤成分信息的量化反演
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摘要
随着土地资源调查的深入,土地资源的管理已从数量管理向质量维护生态保护方向发展,同时向着自动化、定量化方向迈进。利用成像光谱技术的高光谱分辨率特点可提高土地质量评价精度,为土地调查、评价和管理提供更丰富、精确的数据源,对土地资源的管理具有重要的指导意义。高光谱遥感凭借其极高的光谱分辨率在岩石矿物、土壤、植被以及水域等物质组分定量反演研究中表现出非凡的潜力。就土壤而言,其矿物质、有机质、湿度、粗糙度、土壤质地等均是土壤光谱的主要贡献者。
本论文从土壤反射光谱特性入手,揭示土壤物质组分与其光谱成因机理之间的内在联系。运用各种土壤反射率数学变换形式,找出最佳单相关分析波段,利用统计学相关分析方法,建立多组回归分析模型,成功地实现了土壤有机质、湿度、氧化铁的三组分的成像光谱土壤成分填图。具体研究内容如下:
1.通过野外、室内一定数量的光谱测试及其化学成分鉴定,详细地分析和研究山东招远东良乡潮棕壤土光谱特点,认为该土属沙土和沃土的混合。在反射光谱0.45～2.5μm区间范围内,随土壤含水量、有机质以及铁氧化物含量增加,光谱曲线呈下降趋势;而可见光到近红外光是反演土壤有机质的最佳波段。
2.光谱微分处理技术是提取土壤成分信息的有效方法之一。尤其是反射率对数一阶、二阶微分变换可提高土壤有机质、氧化铁与反射率变换值之间的相关性。据此建立线性回归分析模型,完成有机质、氧化铁二组分特征参量填图。同时光谱微分技术还能部分地清除大气效应,特别是一阶微分处理能较好地去除部分线性或接近线性的背景噪声光谱对目标光谱的影响。
3.去包络线法是土壤光谱特征分析的有效方法之一。它简便、准确而有效地提取土壤中某些成分(如:湿度等)的“峰值点”的特征参量。经包络线去除后的土壤光谱反射率归一化到0～1.0之间,可快速有效地突出土壤光谱曲线吸收和反射特性。从而提取“吸收峰”处的特征参量值,如:波段波长位置、深度、宽度、面积等,据此预测土壤中某些组分含量信息。本次研究利用土壤水分在1933nm附近的吸收峰特征值(面积参量)检测土壤含水量(土壤墒情),从而实现了土壤湿度面积填图。
4.定标是定量遥感的基础。辐射定标是通过大气校正来实现的,从而完成相对反射率转换。在此介绍了三种相对反射率转换方法:(1)线性法相对反射率转换,(2)对数残差法相对反射率转换,(3)大气传输模型法相对反射率转换。本次工作采用的是第一种方法。
5.遥感分析的过程就是一个定量反演的过程。如何精确而成功地反演地表或大气组分参量信息是定量遥感研究的热点与难点。除了传感器定标、辐射定标以及大气校正等因素需考虑之外,还应考虑土壤二向反射特性BRDF问题。本文阐述了三种描述土壤二向反射特性模型:经验模型、几何光学模型和辐射传输模型,并对土壤二向反射特性进行了初步探讨。
6.本文还对土壤蒙塞尔颜色模型作为表述土壤重要属性做了一般性的评述。分析认为Munsell模型作为颜色立体模型可对土壤的重要属性作定性或定量分析。如:土壤分类、预测土壤有机质含量或质地等。前人研究证明:土壤明度与土壤反射率有较高的相关性:由于土壤颜色仅限于可见光范围内,因此Munsell颜色立体模型只能预测影响土壤颜色的属性参量,这给定量研究土壤属性带来一定的局限性。
Hyperspectral remote sensing is characterized by high spectrum resolution, and has a great potential to quantitatively identify mineral, soil, vegetation and water content through inverse analysis. Soil spectrum is mainly influenced by the following constituents: mineral, water, organic matter, roughness and texture, etc.Through investigating soil reflectance spectroscopy, this dissertation aims to uncover the genetic relationship between soil constituents and their spectroscopy. The best spectral band is selected based on mathematic analysis of soil reflectance. Several multi-component linear regression models are established through cross-correlation analysis. Soil organic matter, its moisture and its iron oxide content could be spectrally mapped with a great success. This dissertation concentrates on the following aspects.1. Through soil spectral and chemical analysis, it is found that the brown soil in Dongling Xiang, Zhaoyuan, Shangdong is sandy loam. Its reflectance ranges from 0.45 to 2.5 μm. With increasing water, organic matter and iron oxide, the soil reflectance spectral curve goes downward. Visible and near infrared reflectance are the best bands to study the soil organic matter content.2. Spectral derivative analysis is a useful method to investigate the soil constituents. Correlation between soil organic matter/iron oxides and soil reflectance is enhanced by the 1~(st) and 2~(st)derivative of logarithm of reflectance. On this basis, multi-component linear regression model is established, and a map is prepared to display the content of soil organic matter and iron oxide. Additionally, spectral derivative analysis can also partially eliminate atmospheric effect. Especially, the 1~(st) derivative analysis can eliminate some linear or near linear noises affecting the target spectra.3. Continuum-removal is an effective method in soil spectral analysis. Because it extracts the peak absorption eigenvalues of some soil constituents with a simple and an accurate way. After the continuum-removal, the reflectance is normalized to a range of 0-1.0. This enhances the soil absorption and reflection spectrum. The eigenvalues of absorption peak could be easily obtained, such as the wavelength, depth, width and area, etc. These results are used to predict the content of some constituents in the soil. In this study, it is observed that there is a strong absorption around 1450 nm due to water present in the soil. This is used to map the soil moisture.4. Quantitative remote sensing analysis is based upon calibration. Radiance calibration is realized by atmospheric correction, and from which relative reflectan-ce could be derived. Three kinds of relative reflectance are investigated in this study: linear relative reflectivity, residual error of logarithmic relative reflectivity and atmospheric radiance transmit relative reflectivity. The first is used in this investigation.5. The essence of remote sensing is a process of quantitative inversion. How to successfully and accurately extract information on atmospheric and soil constituents through inversion analysis is the hotspot and frontier. Besides sensor calibration, radiance calibration and atmospheric correction, BRDF of soil reflectance property should be taken into account. Available geometric optical model and radiance transmit model are used to investigate the soil BRDF property.6. A general discussion is made on the Munsell model and its properties in this study. This model is a three-dimensional model, and it can be used to qualitatively or quantitatively describe the soil properties, such as soil classification and prediction of organic matter content, etc. Previous study shows that there is a strong correlation between soil brightness and its reflectivity. Since the soil color is determined by the visible light, this sets a limit to use the soil color to investigate the soil properties.
本论文从土壤反射光谱特性入手,揭示土壤物质组分与其光谱成因机理之间的内在联系。运用各种土壤反射率数学变换形式,找出最佳单相关分析波段,利用统计学相关分析方法,建立多组回归分析模型,成功地实现了土壤有机质、湿度、氧化铁的三组分的成像光谱土壤成分填图。具体研究内容如下:
1.通过野外、室内一定数量的光谱测试及其化学成分鉴定,详细地分析和研究山东招远东良乡潮棕壤土光谱特点,认为该土属沙土和沃土的混合。在反射光谱0.45～2.5μm区间范围内,随土壤含水量、有机质以及铁氧化物含量增加,光谱曲线呈下降趋势;而可见光到近红外光是反演土壤有机质的最佳波段。
2.光谱微分处理技术是提取土壤成分信息的有效方法之一。尤其是反射率对数一阶、二阶微分变换可提高土壤有机质、氧化铁与反射率变换值之间的相关性。据此建立线性回归分析模型,完成有机质、氧化铁二组分特征参量填图。同时光谱微分技术还能部分地清除大气效应,特别是一阶微分处理能较好地去除部分线性或接近线性的背景噪声光谱对目标光谱的影响。
3.去包络线法是土壤光谱特征分析的有效方法之一。它简便、准确而有效地提取土壤中某些成分(如:湿度等)的“峰值点”的特征参量。经包络线去除后的土壤光谱反射率归一化到0～1.0之间,可快速有效地突出土壤光谱曲线吸收和反射特性。从而提取“吸收峰”处的特征参量值,如:波段波长位置、深度、宽度、面积等,据此预测土壤中某些组分含量信息。本次研究利用土壤水分在1933nm附近的吸收峰特征值(面积参量)检测土壤含水量(土壤墒情),从而实现了土壤湿度面积填图。
4.定标是定量遥感的基础。辐射定标是通过大气校正来实现的,从而完成相对反射率转换。在此介绍了三种相对反射率转换方法:(1)线性法相对反射率转换,(2)对数残差法相对反射率转换,(3)大气传输模型法相对反射率转换。本次工作采用的是第一种方法。
5.遥感分析的过程就是一个定量反演的过程。如何精确而成功地反演地表或大气组分参量信息是定量遥感研究的热点与难点。除了传感器定标、辐射定标以及大气校正等因素需考虑之外,还应考虑土壤二向反射特性BRDF问题。本文阐述了三种描述土壤二向反射特性模型:经验模型、几何光学模型和辐射传输模型,并对土壤二向反射特性进行了初步探讨。
6.本文还对土壤蒙塞尔颜色模型作为表述土壤重要属性做了一般性的评述。分析认为Munsell模型作为颜色立体模型可对土壤的重要属性作定性或定量分析。如:土壤分类、预测土壤有机质含量或质地等。前人研究证明:土壤明度与土壤反射率有较高的相关性:由于土壤颜色仅限于可见光范围内,因此Munsell颜色立体模型只能预测影响土壤颜色的属性参量,这给定量研究土壤属性带来一定的局限性。
Hyperspectral remote sensing is characterized by high spectrum resolution, and has a great potential to quantitatively identify mineral, soil, vegetation and water content through inverse analysis. Soil spectrum is mainly influenced by the following constituents: mineral, water, organic matter, roughness and texture, etc.Through investigating soil reflectance spectroscopy, this dissertation aims to uncover the genetic relationship between soil constituents and their spectroscopy. The best spectral band is selected based on mathematic analysis of soil reflectance. Several multi-component linear regression models are established through cross-correlation analysis. Soil organic matter, its moisture and its iron oxide content could be spectrally mapped with a great success. This dissertation concentrates on the following aspects.1. Through soil spectral and chemical analysis, it is found that the brown soil in Dongling Xiang, Zhaoyuan, Shangdong is sandy loam. Its reflectance ranges from 0.45 to 2.5 μm. With increasing water, organic matter and iron oxide, the soil reflectance spectral curve goes downward. Visible and near infrared reflectance are the best bands to study the soil organic matter content.2. Spectral derivative analysis is a useful method to investigate the soil constituents. Correlation between soil organic matter/iron oxides and soil reflectance is enhanced by the 1~(st) and 2~(st)derivative of logarithm of reflectance. On this basis, multi-component linear regression model is established, and a map is prepared to display the content of soil organic matter and iron oxide. Additionally, spectral derivative analysis can also partially eliminate atmospheric effect. Especially, the 1~(st) derivative analysis can eliminate some linear or near linear noises affecting the target spectra.3. Continuum-removal is an effective method in soil spectral analysis. Because it extracts the peak absorption eigenvalues of some soil constituents with a simple and an accurate way. After the continuum-removal, the reflectance is normalized to a range of 0-1.0. This enhances the soil absorption and reflection spectrum. The eigenvalues of absorption peak could be easily obtained, such as the wavelength, depth, width and area, etc. These results are used to predict the content of some constituents in the soil. In this study, it is observed that there is a strong absorption around 1450 nm due to water present in the soil. This is used to map the soil moisture.4. Quantitative remote sensing analysis is based upon calibration. Radiance calibration is realized by atmospheric correction, and from which relative reflectan-ce could be derived. Three kinds of relative reflectance are investigated in this study: linear relative reflectivity, residual error of logarithmic relative reflectivity and atmospheric radiance transmit relative reflectivity. The first is used in this investigation.5. The essence of remote sensing is a process of quantitative inversion. How to successfully and accurately extract information on atmospheric and soil constituents through inversion analysis is the hotspot and frontier. Besides sensor calibration, radiance calibration and atmospheric correction, BRDF of soil reflectance property should be taken into account. Available geometric optical model and radiance transmit model are used to investigate the soil BRDF property.6. A general discussion is made on the Munsell model and its properties in this study. This model is a three-dimensional model, and it can be used to qualitatively or quantitatively describe the soil properties, such as soil classification and prediction of organic matter content, etc. Previous study shows that there is a strong correlation between soil brightness and its reflectivity. Since the soil color is determined by the visible light, this sets a limit to use the soil color to investigate the soil properties.
引文
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